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Welcome!

This website provides interactive results for the forecasting models explored in the paper Estimating Neighborhood Asking Rents using Scraped Craigslist Rental Listings.

The goal of this research is to a.) understand the temporal dynamics of rent estimates in Seattle and b.) forecast the current quarter’s rent levels based off of the prior periods. The focal series of models regress median one-bedroom rent asked values on different specifications of the panel’s correlation structure (i.e. temporal and spatial). All of the candidate models’ posterior distributions are estimated with integrated nested Laplace approximations (INLA) using the default, weakly-informative priors for all model hyperparameters. Throughout the following analyses, the training data are 2017 Q2 up to the prior quarter (i.e. 2018 Q1). The test period is a forecast for the current period and includes comparison to the appropriate median rent estimates for data observed so far in this period.

Most graphics include some level of interactivity, usually either hover-over tooltip information or a slider to control various views of the graphic. Clicking on cases will highlight data elements in most graphics, and double-clicking will reset the graphic. You can download the source code and data for this project from Github here.

Contact Chris Hess at hesscl@uw.edu for more information about this research.

This page was last updated: 2018-05-29




Table of Contents

Page Description
Distribution density graphic to investigate the distribution of rents among Seattle neighborhoods for each quarter
Panel Time-Series line graphic to show the observed or modeled temporal structure
Spatial Time-Series series of maps to show observed change across time
Model Fit tables of model root mean square error (RMSE), mean absolute error (MAE), and deviance information criterion (DIC) across training and test data

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Observed vs. Smoothed Rent Estimates

Distribution

Panel Time-Series

Spatial Time-Series

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Observed

Non-Spatial AR(1)

Spatial AR(1)

Spatiotemporal AR(1)

Model Fit

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Model Legend

Model abbr. Description
int Quarter fixed intercept
log(med1B) ~ 1 + Qtr
ns Non-spatial tract random effect for each tract, quarter fixed intercept
log(med1B) ~ 1 + Qtr + f(idtract, model = “iid”)
nsar1 Non-spatial tract random effect, AR(1) random effect for prior quarter, i.i.d random effect for current quarter
log(med1B) ~ 1 + f(idtract, model = “iid”) + f(idqtr, model = “ar1”) + , f(idqtr1, model = “iid”)
bym Spatial intrinsic conditional autoregressive (ICAR) tract random-effect, non-spatial i.i.d tract random effect, AR(1) random effect for prior quarter, i.i.d random effect for current quarter
log(med1B) ~ 1 + f(idtract, model = “bym”, scale.model = T, graph = “./output/seatract.graph”) + , f(idqtr, model = “ar1”) + f(idqtr1, model = “iid”)
spt Spatial intrinsic conditional autoregressive (ICAR) tract random-effect, non-spatial i.i.d tract random effect, AR(1) random effect for prior quarter, i.i.d random effect for current quarter, i.i.d. random effect for tract-quarter (space-time interaction)
log(med1B) ~ 1 + f(idtract, model = “bym”, scale.model = T, graph = “./output/seatract.graph”) + , f(idqtr, model = “ar1”) + f(idqtr1, model = “iid”) + f(idtractqtr, , model = “iid”)

Accuracy and Information Criteria

train_test int_rmse ns_rmse nsar1_rmse bym_rmse spt_rmse
Test 309.4311 218.1297 210.3941 203.1975 226.1912
Training 324.3490 136.1696 136.9117 139.0038 60.6391



train_test int_mae ns_mae nsar1_mae bym_mae spt_mae
Test 253.1582 160.34949 151.20553 146.44060 162.06840
Training 258.1041 89.43893 90.29539 93.72625 40.55311



train_test int_DIC ns_DIC nsar1_DIC bym_DIC spt_DIC
Training -168.1242 -679.9015 -679.7958 -685.2012 -908.9739



train_test int_WAIC ns_WAIC nsar1_WAIC bym_WAIC spt_WAIC
Training -167.4504 -661.0794 -660.4344 -666.5113 -902.9023

Hyperparameters

Non-Spatial AR(1)
mean sd 0.025quant 0.5quant 0.975quant mode
Precision for the Gaussian observations 88.0541 6.6442 75.6237 87.8362 101.7756 87.4460
Precision for idtract 29.8512 4.1501 22.4418 29.5975 38.7549 29.1277
Precision for idqtr 7093.9982 12771.7425 518.2153 3528.3813 35685.9016 1263.1158
Rho for idqtr 0.3063 0.3980 -0.5647 0.3682 0.8880 0.6178
Precision for idqtr1 20111.6959 28250.3371 534.8344 10969.1455 94067.7521 1019.0632



Spatial AR(1)
mean sd 0.025quant 0.5quant 0.975quant mode
Precision for the Gaussian observations 87.5784 6.6397 75.1854 87.3500 101.3150 86.9271
Precision for idtract (iid component) 105.8851 29.9572 59.0502 101.8925 175.9424 94.3891
Precision for idtract (spatial component) 76.2415 23.3932 40.2514 72.9491 131.4488 66.7912
Precision for idqtr 6437.4736 10425.2518 551.1503 3439.0885 30944.3786 1343.5467
Rho for idqtr 0.3100 0.3847 -0.5283 0.3650 0.8819 0.5905
Precision for idqtr1 18012.6347 25105.2440 386.3875 9691.0408 84398.0527 621.3606



Spatiotemporal AR(1)
mean sd 0.025quant 0.5quant 0.975quant mode
Precision for the Gaussian observations 204.8418 33.7269 144.7212 200.6809 292.0743 194.9155
Precision for idtract (iid component) 105.2598 29.6559 59.0305 101.2649 174.6545 93.7649
Precision for idtract (spatial component) 76.3825 23.4291 40.2082 73.1295 131.5603 67.0326
Precision for idqtr 6085.0926 10149.3970 476.5165 3183.2777 29738.2621 1179.4309
Rho for idqtr 0.3084 0.3986 -0.5652 0.3710 0.8895 0.6232
Precision for idqtr1 20500.8191 28629.7393 603.6693 11316.2292 95491.0351 1224.7744
Precision for idtractqtr 155.7107 20.0724 116.6404 154.3883 205.6787 153.8852

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Non-Spatial AR(1)

Spatial AR(1)

Spatiotemporal AR(1)